Statistics > Methodology

Title:Signal Subgraph Estimation Via Vertex Screening

Abstract: Graph classification and regression have wide applications in a variety of
domains. A graph is a complex and high-dimensional object, which poses great
challenges to traditional machine learning algorithms. Accurately and
efficiently locating a small signal subgraph dependent on the label of interest
can dramatically improve the performance of subsequent statistical inference.
Moreover, estimating a signal subgraph can aid humans with interpreting these
results. We present a vertex screening method to identify the signal subgraph
when given multiple graphs and associated labels. The method utilizes
distance-based correlation to screen the vertices, and allows the subsequent
classification and regression to be performed on a small induced subgraph. We
demonstrate that this method is consistent in recovering signal vertices and
leads to better classification performance via theory and numerical
experiments. We apply the vertex screening algorithm on human and murine graphs
derived from functional and structural magnetic resonance images to analyze the
site effects and sex differences.